.png){kind=icon}
.png){kind=icon}
Return
DeepSIC: a deep model For satellite image classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-017-1010-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Satellite Image Classification is the problem of classifying satellite images into their corresponding classes. Using supervised deep learning, we provide an efficient classification mechanism of earth observation imagery captured by satellite. Promising classification results were obtained through Convolution Neural Network. Two sets of experiments were carried out. In the first set, the traditional pre-deep learning era approach was followed: features were extracted first which were then given as input to Support Vector Machines (SVM). In the second set of experiments, the images were directly provided as input to Convolution Neural Networks and the output features were then used as input to the SVM. The proposed schemes were tested on satellite images datasets AID, UC-merced and WHU-RS, having varied classes and heterogeneous image dimensions. We show that our model, with Convolution Neural Network, achieves 92% accuracy on the AID dataset and outperforms the previously reported best accuracy of 89.64% (Xia et al. in CoRR, arXiv: 1608.05167, 2016).
Keywords:
Deep leaning
Satellite images
Classification
Transfer learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K